Active ML for 6G: Towards Efficient Data Generation, Acquisition, and Annotation

• URL: http://arxiv.org/abs/2406.03630v1
• Date: Wed, 5 Jun 2024 21:29:05 GMT
• Title: Active ML for 6G: Towards Efficient Data Generation, Acquisition, and Annotation
• Authors: Omar Alhussein, Ning Zhang, Sami Muhaidat, Weihua Zhuang,
• Abstract summary: This paper explores the integration of active machine learning (ML) for 6G networks. Unlike passive ML systems, active ML can be made to interact with the network environment. We highlight the potential of active learning based 6G networks to enhance computational efficiency.
• Score: 16.27834604691938
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper explores the integration of active machine learning (ML) for 6G networks, an area that remains under-explored yet holds potential. Unlike passive ML systems, active ML can be made to interact with the network environment. It actively selects informative and representative data points for training, thereby reducing the volume of data needed while accelerating the learning process. While active learning research mainly focuses on data annotation, we call for a network-centric active learning framework that considers both annotation (i.e., what is the label) and data acquisition (i.e., which and how many samples to collect). Moreover, we explore the synergy between generative artificial intelligence (AI) and active learning to overcome existing limitations in both active learning and generative AI. This paper also features a case study on a mmWave throughput prediction problem to demonstrate the practical benefits and improved performance of active learning for 6G networks. Furthermore, we discuss how the implications of active learning extend to numerous 6G network use cases. We highlight the potential of active learning based 6G networks to enhance computational efficiency, data annotation and acquisition efficiency, adaptability, and overall network intelligence. We conclude with a discussion on challenges and future research directions for active learning in 6G networks, including development of novel query strategies, distributed learning integration, and inclusion of human- and machine-in-the-loop learning.

Related papers

• Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6G [16.384569776333873]
  "Snake Learning" is a cost-effective distributed learning framework for 6G networks. It sequentially trains the designated part of model layers on individual nodes. It reduces the requirements for storage, memory and communication during the model training phase.
  arXiv  Detail & Related papers  (2024-05-06T11:25:59Z)
• Multiple Access in the Era of Distributed Computing and Edge Intelligence [23.65754442262314]
  We first examine multi-access edge computing (MEC), which is critical to meeting the growing demand for data processing and computational capacity at the edge of the network. We then explore over-the-air (OTA) computing, which is considered to be an approach that provides fast and efficient computation of various functions. The split in between machine learning (ML) and multiple access technologies is also reviewed, with an emphasis on federated learning, reinforcement learning, and the development of ML-based multiple access protocols.
  arXiv  Detail & Related papers  (2024-02-26T11:04:04Z)
• Deep Active Learning for Computer Vision: Past and Future [50.19394935978135]
  Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions. By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies.
  arXiv  Detail & Related papers  (2022-11-27T13:07:14Z)
• Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation [51.21190751266442]
  Domain adaptation (DA) tries to tackle the scenarios when the test data does not fully follow the same distribution of the training data. By learning from large-scale unlabeled samples, self-supervised learning has now become a new trend in deep learning. We propose a novel textbfSelf-textbfSupervised textbfGraph Neural Network (SSG) to enable more effective inter-task information exchange and knowledge sharing.
  arXiv  Detail & Related papers  (2022-04-08T03:37:56Z)
• Lifelong Adaptive Machine Learning for Sensor-based Human Activity Recognition Using Prototypical Networks [0.0]
  Continual learning, also known as lifelong learning, is an emerging research topic that has been attracting increasing interest in the field of machine learning. We build on recent advances in the area of continual machine learning and design a lifelong adaptive learning framework using Prototypical Networks, LAPNet-HAR. LAPNet-HAR processes sensor-based data streams in a task-free data-incremental fashion and mitigates catastrophic forgetting using experience replay and continual prototype adaptation.
  arXiv  Detail & Related papers  (2022-03-11T00:57:29Z)
• Distributed Learning in Wireless Networks: Recent Progress and Future Challenges [170.35951727508225]
  Next-generation wireless networks will enable many machine learning (ML) tools and applications to analyze various types of data collected by edge devices. Distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges. This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks.
  arXiv  Detail & Related papers  (2021-04-05T20:57:56Z)
• Adaptive Scheduling for Machine Learning Tasks over Networks [1.4271989597349055]
  This paper examines algorithms for efficiently allocating resources to linear regression tasks by exploiting the informativeness of the data. The algorithms developed enable adaptive scheduling of learning tasks with reliable performance guarantees.
  arXiv  Detail & Related papers  (2021-01-25T10:59:00Z)
• Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
  Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. In this article, we identify the key requirements and challenges of edge-native AI in 6G.
  arXiv  Detail & Related papers  (2020-10-01T02:16:40Z)
• A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning [115.75967665222635]
  Ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications. Deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks. This tutorial illustrates how domain knowledge can be integrated into different kinds of deep learning algorithms for URLLC.
  arXiv  Detail & Related papers  (2020-09-13T14:53:01Z)
• Bayesian active learning for production, a systematic study and a reusable library [85.32971950095742]
  In this paper, we analyse the main drawbacks of current active learning techniques. We do a systematic study on the effects of the most common issues of real-world datasets on the deep active learning process. We derive two techniques that can speed up the active learning loop such as partial uncertainty sampling and larger query size.
  arXiv  Detail & Related papers  (2020-06-17T14:51:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.